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of using FPGA tools and provide them the necessary training to use the ASIC tools. The team at Cambridge consists of three investigators: Prof. Robert Mullins (PI), Prof. Timothy Jones and Dr Rika Antonova
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, Computer Science, Experimental Subatomic Physics, or related fields. Relevant research experience in firmware programming (e.g. with VHDL) and resources optimization for specific FPGA architectures
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topics. Candidates should have some experience working with FPGAs as well as an understanding of computer networks. Experience with both RTL and HLS design is favoured. The ideal candidate would have some
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multilayer PCB, FPGA programming, embedded systems, and preferably ASIC-design. Knowledge in Systems Engineering, particularly in Space and Defence is highly regarded. You will also demonstrate personal
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, such as GPUs and FPGAs, to offloading applications in a seamless and portable way. This includes implementing runtime logic and resource scheduling strategies that can leverage available hardware
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, PSIM, Proteus, LabVIEW, SketchUp, SolidWorks, etc. Knowledge of microcontrollers, STM32, FPGAs, etc. Knowledge of communication protocols such as I2C, SPI, Profibus, Modbus, CAN, MQTT, and HTTP
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implementing quality assurance and quality control (QA/QC) test Designing testbenches, and contributing to firmware programming for state-of-the-art FPGA architectures, primarily Intel FPGAs. Collaborating
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of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard
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turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full
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strong background in software development (Python, C++) and microscope control. • Experience FPGA programming is a beneficial. • Training and supervision will be provided throughout the project, but